Rancang Bangun Mesin Pemipil Jagung Dua Silinder menggunakan Motor Listrik

<p class="icsmheading1"><em>Seeing the situation of animal feed needs which tend to increase every year, it must be accompanied by the procurement of production machines to reduce the productivity of corn. Corn sheller machine is a tool that serves to release corn kernels from the cob. Along with technological developments that continue to progress, many production machines have sprung up such as corn shellers ranging from those that are operated manually to automatic. Each machine has different advantages and disadvantages. In the design of this corn sheller machine the main driver uses an electric motor. The design of this corn sheller machine aims to help ease the burden on corn farmers in particular. At first the corn shelling was done manually, then it developed using a large selep machine but the costs incurred were relatively expensive. The design of a two-cylinder corn sheller machine uses an electric motor to determine the appropriate design and construction of the corn sheller machine. In this machine there is no corn diameter limit for the shelling process in other words this machine is designed for all sizes of corn. In this design using several stages, namely first planning the design of the engine frame, electric motor, pulley and v-belt ratio and the calculation of the shaft. The result of the design is a machine design that is 1200 mm long, 1200 mm wide and 800 mm high. The sheller machine is driven using an electric motor of 500 watts, Rpm 1400 with a transmission system using a pulley and a v-belt ratio of 1: 1. There is no limit to the diameter of corn kernels in the shelling process using this machine. So it can be concluded that this machine has an effective design and sufficient shelling results, able to reduce post-harvest costs for corn farmers.</em></p>

Rnase A Enzyme Modification of Optimized SDS Protocol for DNA Extraction Suitable for Real-Time PCR Screening of GMOs

As the number of genetically modified crops increases rapidly, their accurate detection is significant for labelling and safety assessment. Currently, real-time PCR is the “golden standard” method for GMO detection. Hence, extraction of high quality DNA represents a crucial step for accurate and efficient DNA amplification. For GMO presence evaluation in the extracted DNA from raw corn kernels and roasted soybean, we used real-time PCR method, in consistent with the ISO17025 accreditation standards. As for DNA extraction, modified basic SDS protocol by adding RNase A enzyme in different steps of the protocol, with different time and temperature of incubation was used. The results showed as most suitable, the protocol where 10 µl of RNase A enzyme was added together with the lysis buffer at 65 °C for 30 minutes. Data for DNA yield and purity for roasted soybean was 469.6±3.3 µg/ml with A260/280 absorbance ratio 1.78±0.01. Suitability of DNA extracts for GMO analysis was assessed by screening for the presence of 35S promotor and Tnos terminator. Diluted extracts in concentrations 10, 1, 0.1, 0.01 and 0.0027 ng/µl, were tested in six replicates. Positive signal of amplification (LOD) was detected in all concentrations for both genetic elements in both matrices. The LOQ for 35S and Tnos for both matrices was 0.1 ng, while for Tnos in raw corn kernels was 0.01 ng. This in-house developed DNA extraction method is simple and obtains high-quality DNA suitable for GMO screening of 35S promotor and Tnos terminator in both raw and processed matrices.

Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems with existing detection methods for judging the integrity of corn kernels, such as low accuracy, poor reliability, and difficulty in adapting to the complicated and changeable harvesting environment, this paper investigates a broken corn kernel detection device for combine harvesters by using the yolov4-tiny model. Hardware construction is first designed to acquire continuous images and processing of corn kernels without overlap. Based on the images collected, the yolov4-tiny model is then utilized for training recognition of the intact and broken corn kernels samples. Next, a broken corn kernel detection algorithm is developed. Finally, the experiments are carried out to verify the effectiveness of the broken corn kernel detection device. The laboratory results show that the accuracy of the yolov4-tiny model is 93.5% for intact kernels and 93.0% for broken kernels, and the value of precision, recall, and F1 score are 92.8%, 93.5%, and 93.11%, respectively. The field experiment results show that the broken kernel rate obtained by the designed detection device are in good agreement with that obtained by the manually calculated statistic, with differentials at only 0.8%. This study provides a technical reference of a real-time method for detecting a broken corn kernel rate.

EFFECTS OF VERMICOMPOST, CHEMICAL FERTILIZER AND DIFFERENT BIOPESTICIDES ON THE AGRONOMIC CHARACTERISTICS, SENSORY QUALITY AND GRAIN PHYTOCHEMICAL CONTENTS OF CORN (ZEA MAYS)

This study was conducted to look for alternative measures to sustain the profitability of cultivating green corn by using different fertilizers and biopesticides. 2x5 factorial experiment in split-plot in Randomized Complete Block Design with four replication this was conducted at Brgy. Matikiw, Pakil Laguna fromDecember 2019 to March 2020. With the following treatments.A1 – vernicompost + urea and A2 –Chemical fertilizer, B1 – Control, B2 - Kakawate leaf extract (Gliricidiasepium), B3 - Makabuhay vine extract (Tinosporarumphii), B4 - Tagbak leave extract (Alipiniamalaccensis) and B5 - Acapulco leaf extract (Cassia alata).Result revealed that there was no interaction effect between biopesticide and fertilizer materials in growth and yield characteristics of corn plants. There is no interaction effect between biopesticide and different fertilizer materials on the growth characteristics in terms of number of days from sowing to emergence, silking to harvesting, weekly plant height and in all of parameter in yield components of green corn. The earliest emergence, earliest number of days from silking to harvesting, highest number of corn ear, highest biological yield, longest length were observed on the green corn with application of vermicompost + urea while earliest number of days from emergence to silking, tallest height, largest diameter, most number of corn kernels were observed on the green corn with application of farmers practice regardless of biopesticides. The study recommends further testing on the application of vermicompost + urea and acapulco, tagbak and kakawate leaf extracts at different levels of concentration is recommended.

Dissemination of corn sheller machines to increase productivity and efficiency for corn farmer associations in Wonogiri Regency

Corn is a superior agricultural product for the Giri Harjo I farmer group, Girikikis Village, Giriwoyo District, Wonogiri. However, farmers process and peel corn manually, so it takes a more time and inefficient. In order to overcome this problem, this community service designed and made a corn sheller machine that was used to simplify and increase the productivity in the corn harvesting process. This service activity begins with the delivery of the corn sheller machine to the farmer group, then exposure and training on the use of the machine. Corn shelling is accomplished by inserting the corn into the sheller shaft, after which the grinding knife separates the corn kernels from the cob. The shelling test results show that this machine works well, is practical to use, is highly portable, the production process is faster, the corncobs are not damaged, and the electric power consumption is low. This machine has a 0.5 HP motor and a production capacity of 183 kg/hour for shelling corn kernels.

A Statistical Optimization of Convective Drying of Corn Kernels in a Fluidized Bed Dryer

Food storage is an essential process for food security and it needs to be free from any biological contamination. For the same, agriculture produce needs to be completely dried before sending for storage. The present work discusses a systematic approach to model drying parameters of corn kernels in a fluidized bed dryer. Experiments were designed according to a higher level Box-Behnken design combined with response surface methodology. Four parameters were chosen to vary namely: amount of corn kernels (50 -100 gm), temperature of drying (50 – 80⁰C), air velocity (6.01 – 8.08 m/s) and drying time (30 – 60 min) for experiments as well as for the model. The reduction of moisture content was determined after each experiment for understanding the behaviour of drying process. The model equations were obtained and surface response plots were generated in MATLAB to investigate the drying behaviour of corn kernels with all four parameters. Ultimately, this work represents the dependence of moisture removal on all four parameters chosen with efficient use of response surface methodology and Box-Behnken design. Analysis of variance confirmed that velocity of air and amount of corn are the most significant parameters along with temperature and time of drying. Optimum condition with the model were obtained as 50 gm of corn kernels, 80 ⁰C drying temperature, 8 m/sec velocity of air, and 60 min time of drying for 73.3 % of moisture from corn kernels.

Training on the use of corn sheller machine in Kutai Lama Village, Kutai Kartanegara

The corn sheller machine used by the Kutai Lama Village farmer group produces corn cobs that are cut or crushed, so it cannot be used as a medium for making mushrooms and worms. To solve this problem, the team developed a corn sheller machine and trained farmer groups to use the product. In this activity, two corn shelling machines were handed over and given training on how to use a corn sheller machine which is equipped with a 'belt cover' as a safety and added with a 'guide tool' so that the corn kernels do not spill. In addition, the machine is proven to increase the productivity of corn shelling activities. When compared with the manual method, the productivity increases to 4800%.

Transcriptome-wide association and prediction for carotenoids and tocochromanols in fresh sweet corn kernels

Sweet corn is consistently one of the most highly consumed vegetables in the U.S., providing a valuable opportunity to increase nutrient intake through biofortification. Significant variation for carotenoid (provitamin A, lutein, zeaxanthin) and tocochromanol (vitamin E, antioxidants) levels is present in temperate sweet corn germplasm, yet previous genome-wide association studies (GWAS) of these traits have been limited by low statistical power and mapping resolution. Here, we employed a high-quality transcriptomic dataset collected from fresh sweet corn kernels to conduct transcriptome-wide association studies (TWAS) and transcriptome prediction studies for 39 carotenoid and tocochromanol traits. In agreement with previous GWAS findings, TWAS detected significant associations for four causal genes, β-carotene hydroxylase (crtRB1), lycopene epsilon cyclase (lcyE), γ-tocopherol methyltransferase (vte4), and homogentisate geranylgeranyltransferase (hggt1) on a transcriptome-wide level. Pathway-level analysis revealed additional associations for deoxy-xylulose synthase2 (dxs2), diphosphocytidyl methyl erythritol synthase2 (dmes2), cytidine methyl kinase1 (cmk1), and geranylgeranyl hydrogenase1 (ggh1), of which, dmes2, cmk1, and ggh1 have not previously been identified through maize association studies. Evaluation of prediction models incorporating genome-wide markers and transcriptome-wide abundances revealed a trait-dependent benefit to the inclusion of both genomic and transcriptomic data over solely genomic data, but both transcriptome- and genome-wide datasets outperformed a priori candidate gene-targeted prediction models for most traits. Altogether, this study represents an important step towards understanding the role of regulatory variation in the accumulation of vitamins in fresh sweet corn kernels.